Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A data conversion and distribution system comprising: a data subscription unit having at least one data interface communicatively coupled to a plurality of data source devices and configured to receive data from the plurality of data source devices and to transmit the data via secure communication over at least one network; and a virtual machine communicatively coupled to the data subscription unit, the virtual machine comprising one or more servers, a non-transitory memory, and one or more processors comprising machine readable instructions, the virtual machine configured to: receive the data from the data subscription unit, apply one or more filters and/or conditions to the data to generate backtesting data, run the backtesting data through one or more non-statistical models to generate one or more metrics, run the backtesting data through one or more statistical models to generate one or more relationship coefficients based on statistically significant features of the backtesting data, determine one or more relationships amongst the backtesting data based on, at least, the one or more relationship coefficients, generate backtesting results based on the one or more metrics and the one or more relationships, translate the backtesting results into one or more interactive visuals, generate a graphical user interface (GUI) for displaying the backtesting results and the one or more interactive visuals on a user device, receive, via the GUI, input from a user device, adjust, based on the input from the user device, the one or more filters and/or conditions, apply the adjusted one or more filters and/or conditions to the backtesting results to generate updated backtesting results without re-running the one or more statistical models and the one or more non-statistical models, and update one or more of the displayed backtesting results and the one or more interactive visuals based on the updated backtesting results.
The system is designed for data conversion and distribution, particularly for financial or analytical applications requiring backtesting. It addresses the challenge of efficiently processing large datasets from multiple sources, applying analytical models, and providing interactive visualization of results. The system includes a data subscription unit that collects data from various sources via secure communication and a virtual machine that processes this data. The virtual machine applies filters and conditions to generate backtesting data, which is then analyzed using both non-statistical and statistical models. Non-statistical models produce metrics, while statistical models identify statistically significant features to derive relationship coefficients. These coefficients help determine relationships within the data. The system generates backtesting results, translates them into interactive visuals, and displays them via a graphical user interface (GUI). Users can adjust filters and conditions through the GUI, triggering updates to the visuals and results without re-running the models, improving efficiency. The system enables real-time data analysis and visualization, supporting dynamic decision-making.
2. The system of claim 1 , wherein the virtual machine further comprises: a data unification module configured to reformat and aggregate the data from the data subscription unit prior to providing it to the data conversion module.
A system for managing and processing data in a virtualized environment addresses the challenge of integrating and standardizing diverse data sources within a virtual machine. The system includes a virtual machine with a data subscription unit that collects data from multiple external sources, which may vary in format, structure, and protocol. To ensure compatibility and consistency, the virtual machine incorporates a data unification module that reformats and aggregates the collected data before passing it to a data conversion module. The data conversion module then transforms the unified data into a standardized format suitable for further processing or analysis. This approach streamlines data handling, reduces complexity, and ensures seamless integration of heterogeneous data streams within the virtualized system. The unified data structure enables efficient processing, analysis, and decision-making, improving overall system performance and reliability. The system is particularly useful in environments where data from multiple sources must be consolidated and processed in a consistent manner, such as in cloud computing, enterprise applications, or IoT ecosystems.
3. The system of claim 2 , wherein the data unification module is further configured to at least one of decompress, cleanse, and unpack the data.
A system for data processing and analysis includes a data unification module that integrates data from multiple sources into a standardized format. The system addresses challenges in handling diverse, fragmented, or inconsistent data by ensuring compatibility and usability across different applications. The data unification module is designed to perform additional preprocessing steps, including decompressing compressed data to restore it to its original form, cleansing data to remove errors, duplicates, or irrelevant information, and unpacking structured or nested data into a more accessible format. These preprocessing steps enhance data quality and facilitate seamless integration into downstream analytical processes. The system may also include a data ingestion module for collecting data from various sources and a data storage module for organizing and storing the unified data. By standardizing and preprocessing data, the system improves efficiency in data analysis, reporting, and decision-making.
4. The system of claim 1 , wherein the one or more metrics comprise one or more of security level metrics, weekly aggregate statistics, and time dependent aggregate statistics.
This invention relates to a system for monitoring and analyzing security and performance metrics in a computing environment. The system addresses the challenge of effectively tracking and evaluating various security and performance indicators to enhance system reliability and threat detection. The core system includes components for collecting, processing, and displaying metrics related to security levels, weekly aggregate statistics, and time-dependent aggregate statistics. Security level metrics assess the vulnerability or risk status of the system, while weekly aggregate statistics provide a summary of security events or performance data over a seven-day period. Time-dependent aggregate statistics offer insights into trends or patterns by analyzing metrics over specific time intervals. The system dynamically adjusts its monitoring parameters based on these metrics to improve real-time decision-making and proactive threat mitigation. By integrating these diverse metrics, the system enables comprehensive monitoring and adaptive responses to security and performance issues, ensuring robust system protection and operational efficiency.
5. The system of claim 1 , wherein the determining the one or more relationships comprises one or more of: determining statistically significant features; generating relationship coefficients and applying the relationship coefficients to a first portion of the backtesting data having similar features as a second portion of the backtesting data; generating optimal trade size projections, the optimal trade size projections comprising a minimum transaction size where trading activity above this level, at any trade size, demonstrates a statistically insignificant variation in market prices; generating market implied bid-ask spreads, the market implied bid-ask spreads comprising a projected difference between a bid price and an ask price for securities without observable trade data; and generating comparable data groupings, the comparable data groupings comprising a representation of similar bonds determined by one or more statistical and machine learning algorithms.
This invention relates to a system for analyzing financial market data to determine relationships between different securities, particularly bonds, and optimizing trading strategies. The system addresses the challenge of accurately assessing market behavior and trade impacts in environments with limited observable data, such as bonds with infrequent trading activity. The system determines relationships between securities by identifying statistically significant features, generating relationship coefficients to compare similar data portions, and projecting optimal trade sizes where trading volume does not significantly affect market prices. It also calculates market-implied bid-ask spreads for securities lacking observable trade data and groups comparable bonds using statistical and machine learning algorithms. These analyses help traders and investors make data-driven decisions by quantifying market dynamics and reducing uncertainty in pricing and execution strategies. The system leverages historical backtesting data to validate its projections and improve accuracy in real-world applications.
6. The system of claim 1 , wherein the GUI comprises a results dashboard displaying the each visual/interactive graph of the one or more interactive visuals in a corresponding one or more windows and the backtesting results as backtest details.
This invention relates to a data analysis system that provides interactive visualizations and backtesting capabilities for evaluating financial or analytical models. The system addresses the challenge of presenting complex data in an intuitive, interactive format while allowing users to test model performance against historical data. The system includes a graphical user interface (GUI) that displays a results dashboard. The dashboard features multiple interactive visualizations, each presented in a separate window. These visuals allow users to explore data dynamically, adjusting parameters and observing real-time updates. The GUI also shows backtesting results, providing detailed performance metrics and historical comparisons. This enables users to assess how well a model would have performed under past conditions, aiding in validation and refinement. The interactive visuals may include charts, graphs, or other data representations that respond to user inputs, such as selections or adjustments to variables. The backtest details include key performance indicators, risk metrics, and other relevant data to evaluate model accuracy and reliability. By integrating these features, the system enhances decision-making by combining interactive exploration with rigorous historical testing.
7. The system of claim 6 , wherein the one or more windows comprise a magnifier feature that, in response to user input to the GUI, expands the corresponding visual/interactive graph of the one or more interactive visuals to a larger size.
This invention relates to graphical user interfaces (GUIs) for data visualization systems, specifically addressing the challenge of displaying detailed information in interactive visualizations without overwhelming the user. The system provides a magnifier feature within one or more windows of the GUI, allowing users to expand a selected visual or graph to a larger size in response to user input. This magnifier feature enhances readability and interaction with complex or densely populated data visualizations by enabling closer inspection of specific elements. The system may include multiple interactive visuals, such as charts, graphs, or diagrams, each capable of being magnified independently. The magnifier feature dynamically adjusts the display size of the selected visual while maintaining the context of the surrounding interface, ensuring users can focus on detailed data without losing the broader visualization context. This improves usability by providing a seamless way to explore fine-grained details in interactive data representations. The invention is particularly useful in applications requiring precise data analysis, such as scientific research, financial modeling, or engineering simulations.
8. The system of claim 6 , wherein the one or more interactive visuals comprise a graph illustrating proximity to trade, a graph illustrating a proximity to trade by week, a graph illustrating a distance reduction time series trend analysis, a graph illustrating price percentage distribution analysis, and a graph illustrating an absolute distance reduction days since last trade.
This invention relates to a system for analyzing and visualizing trade proximity data in financial markets. The system addresses the challenge of providing traders and analysts with clear, actionable insights into trade execution efficiency and market behavior. The core system includes a data processing module that collects and processes trade data, such as execution times, distances from target prices, and historical trade patterns. The processed data is then presented through a user interface featuring interactive visualizations that help users assess trade performance and market dynamics. The interactive visuals include a graph showing proximity to trade, which displays how close executed trades are to target prices. Another graph illustrates proximity to trade by week, allowing users to track weekly trends in execution efficiency. A distance reduction time series trend analysis graph provides a historical view of how trade distances from target prices have evolved over time. Additionally, a price percentage distribution analysis graph shows the distribution of executed prices relative to target prices, helping users identify patterns in price deviations. Finally, a graph illustrating absolute distance reduction days since last trade measures how quickly trades are converging toward target prices over time. By combining these visualizations, the system enables users to monitor trade execution quality, identify inefficiencies, and make data-driven decisions to optimize trading strategies. The interactive nature of the visuals allows for dynamic exploration of the data, enhancing user engagement and insight generation.
9. The system of claim 6 , wherein the backtest details comprise one or more categories of the backtesting results, including CUSIP identification, asset class, sector, number of observations, the one or more categories having a filter for receiving the user input initiating the update the one or more of the displayed backtesting results and the one or more interactive visuals.
The system relates to financial data analysis, specifically backtesting investment strategies. Backtesting involves evaluating the performance of a trading strategy using historical data to simulate how it would have performed in the past. A key challenge in this domain is efficiently organizing and filtering large volumes of backtesting results to allow users to analyze specific subsets of data. The system provides a backtesting platform that includes interactive visualizations and detailed filtering capabilities. The backtesting results are categorized into one or more groups, such as CUSIP identification, asset class, sector, and the number of observations. Each category includes a filter that allows users to input criteria to update the displayed results and visualizations in real time. For example, a user could filter results by asset class to focus on equities or fixed income, or by sector to analyze performance within specific industries. The system dynamically adjusts the visual representations of the data, such as charts or graphs, based on the applied filters, enabling users to explore different aspects of the backtesting results interactively. This enhances the ability to assess strategy performance across various dimensions and refine investment decisions.
10. The system of claim 6 , wherein, in response to a user selecting a portion of a visual/interactive graph via an input to the GUI, the data conversion module is further configured to: generate a new visual/interactive graph for that portion; and display the new visual/interactive graph in the one or more windows.
This invention relates to data visualization systems that enhance user interaction with visual/interactive graphs. The problem addressed is the difficulty users face when analyzing large or complex datasets presented in a single graph, as it can be challenging to focus on specific portions of the data without losing context or requiring manual adjustments. The system includes a graphical user interface (GUI) that displays one or more windows containing visual/interactive graphs representing data. A data conversion module processes the data to generate these graphs. When a user selects a portion of a graph via an input to the GUI, the system dynamically generates a new visual/interactive graph for the selected portion. This new graph is then displayed in one or more windows, allowing the user to zoom in on the selected data while maintaining the ability to interact with it. The system ensures that the new graph retains the same interactive features as the original, such as tooltips, filtering, or other user-driven modifications. This approach improves data analysis by providing a seamless way to explore detailed subsets of data without disrupting the overall visualization workflow. The invention is particularly useful in applications requiring deep data inspection, such as scientific research, financial analysis, or business intelligence.
11. The system of claim 1 , wherein the data conversion module is further configured to: generate a hyperlink for display in the GUI; and in response to the hyperlink being selected via an input to the GUI, generate a pop-up window to appear within a widget displayed in the GUI, the pop-up window comprising contextual information about the data having the plurality of data formats.
This invention relates to a data visualization system that processes and displays data in multiple formats within a graphical user interface (GUI). The system addresses the challenge of presenting complex, multi-format data in a user-friendly manner, ensuring clarity and accessibility. A key component is a data conversion module that transforms data into a standardized format for visualization. The module generates hyperlinks within the GUI, which, when selected, trigger the display of a pop-up window. This pop-up appears within a widget in the GUI and provides contextual information about the underlying data, enhancing user understanding. The system dynamically adapts to different data formats, ensuring seamless integration and interaction. The hyperlink and pop-up window functionality allow users to access detailed insights without navigating away from the primary interface, improving efficiency and usability. The invention is particularly useful in applications requiring real-time data analysis and visualization, such as financial dashboards, scientific research tools, or business intelligence platforms. By embedding contextual information directly within the GUI, the system reduces cognitive load and streamlines data interpretation.
12. The system of claim 1 , wherein the GUI comprises a first page, and wherein the update comprises generating a second page in addition to the first page, the second page displaying the updated backtesting results and the one or more updated interactive visuals.
This invention relates to a graphical user interface (GUI) system for financial analysis, specifically for displaying backtesting results and interactive visuals. The system addresses the challenge of efficiently presenting updated financial data and analysis in a clear, interactive format. The GUI includes a first page that initially displays backtesting results and interactive visuals, such as charts or graphs, which allow users to explore financial data dynamically. When new data or updates are available, the system generates a second page in addition to the first page. This second page displays the updated backtesting results and one or more updated interactive visuals, ensuring that users can compare historical and current data side by side. The system may also include a data processing module that retrieves and processes financial data, as well as a visualization engine that generates the interactive visuals based on the processed data. The GUI may further include navigation controls to switch between the first and second pages, allowing users to easily access and compare different versions of the analysis. This approach enhances user experience by providing a structured, organized way to review updated financial insights without overwriting previous data.
13. The system of claim 1 , wherein the update comprises displaying detailed information about the one or more of the updated backtesting results and the one or more updated interactive visuals in response to the user selecting said one or more of the backtesting results and the one or more interactive visuals via the GUI.
This invention relates to financial data analysis systems, specifically improving user interaction with backtesting results and visualizations. The system addresses the challenge of efficiently conveying detailed insights from financial backtesting, where users often need to explore specific results or visualizations in depth. The core system includes a graphical user interface (GUI) that presents backtesting results and interactive visuals, such as charts or graphs, derived from historical financial data analysis. When a user selects one or more of these results or visuals, the system dynamically updates the display to show detailed information related to the selected items. This detailed information may include additional metrics, underlying data points, or contextual explanations that enhance the user's understanding of the backtesting outcomes. The interactive visuals allow users to manipulate the data presentation, such as zooming, filtering, or adjusting parameters, to further refine their analysis. By providing on-demand detailed information in response to user selections, the system streamlines the analytical process, reducing the need for separate queries or manual data extraction. This approach enhances decision-making by making complex financial data more accessible and actionable.
14. The system of claim 13 , wherein the detailed information is displayed in a window within the GUI.
A system for displaying detailed information within a graphical user interface (GUI) is designed to enhance user interaction with complex data sets. The system addresses the challenge of presenting detailed information in a way that is both accessible and non-disruptive to the user's workflow. The GUI includes a primary display area for general information and a secondary window for detailed information. The secondary window is dynamically generated and positioned within the GUI based on user interaction, such as hovering over or selecting an element in the primary display. The detailed information window can be resized, repositioned, or minimized to allow users to focus on specific data without navigating away from the main interface. The system ensures that the detailed information remains contextually relevant to the user's current task, improving efficiency and reducing cognitive load. The window may also include interactive elements, such as buttons or links, to further explore related data or perform actions. This approach optimizes the presentation of detailed information while maintaining a seamless user experience.
15. The system of claim 13 , wherein the GUI comprises a first page and the detailed information is displayed on a second page.
A system for displaying information in a graphical user interface (GUI) is designed to improve user interaction with complex data sets. The system addresses the challenge of presenting detailed information in a structured and accessible manner, particularly in applications where users need to navigate between high-level summaries and detailed data. The GUI includes a first page that provides an overview or summary of the information, allowing users to quickly assess key details. When a user selects an item from the first page, the system transitions to a second page that displays the detailed information associated with the selected item. This two-page structure enhances usability by separating broad overviews from in-depth data, reducing cognitive load and improving navigation efficiency. The system may also include features such as filtering, sorting, or search functionality on the first page to help users locate relevant items before accessing detailed information. The detailed information on the second page may be presented in a structured format, such as tables, charts, or interactive elements, to facilitate analysis and decision-making. This approach is particularly useful in applications like data analytics, inventory management, or user profile systems where users frequently switch between summary and detailed views.
16. A method for converting and distributing data comprising: receiving, by a data subscription unit having at least one data interface communicatively coupled to a plurality of data source devices, data from the plurality of data source devices; transmitting, by the data subscription unit, the data via secure communication over at least one network; receiving, by a data receiver module of a virtual machine communicatively coupled to the data subscription unit, the data from the data subscription unit, the virtual machine comprising one or more servers, a non-transitory memory, and one or more processors comprising machine readable instructions; applying, by the virtual machine, one or more filters and/or conditions to the data to generate backtesting data; running, by the virtual machine, the backtesting data through one or more non-statistical models to generate one or more metrics; running, by the virtual machine, the backtesting data through one or more statistical models to generate one or more relationship coefficients based on statistically significant features of the backtesting data; determining, by the virtual machine, one or more relationships amongst the backtesting data based on, at least, the one or more relationship coefficients; generating, by the virtual machine, backtesting results based on the one or more metrics and the one or more relationships; translating, by the virtual machine, the backtesting results into one or more interactive visuals; generating, by the virtual machine, a graphical user interface (GUI) for displaying the backtesting results and the one or more interactive visuals on a user device; receiving, via the GUI, input from a user device; adjusting, by the virtual machine based on the input from the user device, the one or more filters and/or conditions; applying, by the virtual machine, the adjusted one or more filters and/or conditions to the backtesting results to generate updated backtesting results without re-running the one or more statistical models and the one or more non-statistical models; and updating, by the virtual machine, one or more of the displayed backtesting results and the one or more interactive visuals based on the updated backtesting results.
This invention relates to a system for converting, analyzing, and visualizing data from multiple sources. The system collects data from various devices through a data subscription unit, which securely transmits the data over a network to a virtual machine. The virtual machine processes the data by applying filters and conditions to generate backtesting data. It then runs this data through both non-statistical and statistical models to produce metrics and relationship coefficients based on statistically significant features. The system determines relationships within the data and generates backtesting results, which are translated into interactive visuals displayed on a user device via a graphical user interface (GUI). Users can adjust filters and conditions through the GUI, and the system updates the results and visuals without re-running the models, improving efficiency. The virtual machine includes servers, memory, and processors executing machine-readable instructions to perform these operations. This approach enables dynamic data analysis and visualization with real-time user interaction.
17. The method of claim 16 , further comprising: reformatting and aggregating, by a data unification module of the virtual machine, the data from the data subscription unit prior to providing it to the data conversion module.
A system and method for processing and converting data within a virtual machine environment addresses the challenge of integrating and standardizing diverse data sources for analysis. The system includes a data subscription unit that collects data from multiple sources, a data unification module that reformats and aggregates the collected data into a standardized format, and a data conversion module that transforms the unified data into a target format suitable for further processing or analysis. The data unification module ensures consistency by normalizing data structures, handling missing values, and resolving conflicts between different data sources. The data conversion module then applies predefined rules or algorithms to convert the unified data into the desired output format, such as structured tables, JSON, or other machine-readable formats. This approach enables seamless integration of heterogeneous data streams, improving data quality and interoperability in virtualized environments. The system is particularly useful in cloud computing, big data analytics, and enterprise applications where data from multiple sources must be processed efficiently and accurately.
18. The method of claim 17 , further comprising: performing, by the data unification module, at least one of decompressing, cleansing, and unpacking the data.
This invention relates to data processing systems that unify data from multiple sources. The problem addressed is the difficulty of integrating diverse data formats, structures, and quality levels into a consistent, usable dataset. The system includes a data unification module that processes incoming data to standardize it for analysis or further use. The module performs operations such as decompressing, cleansing, and unpacking the data to ensure it is in a usable form. Decompression involves reducing file sizes by reversing compression techniques applied to the original data. Cleansing removes errors, inconsistencies, or irrelevant information, improving data accuracy and reliability. Unpacking involves extracting data from container formats, such as archives or structured files, to access the underlying content. These preprocessing steps ensure the data is properly formatted and free of corruption before unification. The system may also include additional modules for data transformation, enrichment, or validation, depending on the specific application. The invention is particularly useful in environments where data must be aggregated from disparate sources, such as enterprise systems, IoT networks, or cloud-based platforms, to enable consistent analysis or decision-making.
19. The method of claim 16 , wherein the one or more metrics comprise one or more of security level metrics, weekly aggregate statistics, and time dependent aggregate statistics.
This invention relates to a system for monitoring and analyzing security metrics in a computing environment. The system collects and processes various types of security-related data to provide insights into system vulnerabilities, threats, and overall security posture. The method involves gathering security level metrics, which quantify the effectiveness of security measures in place, such as encryption strength, access control policies, and intrusion detection rates. Additionally, the system computes weekly aggregate statistics to track security trends over time, identifying patterns or anomalies that may indicate emerging threats. Time-dependent aggregate statistics are also generated, allowing for real-time or near-real-time analysis of security events, such as login attempts, data access, and system breaches. These metrics are used to assess the security health of the system, detect potential vulnerabilities, and recommend corrective actions. The system may integrate with existing security tools to enhance threat detection and response capabilities, providing a comprehensive view of security risks across the computing environment. By analyzing these metrics, organizations can proactively mitigate threats and improve their security defenses.
20. The method of claim 16 , wherein the determining the one or more relationships comprises one or more of: determining statistically significant features; generating relationship coefficients and applying the relationship coefficients to a first portion of the backtesting data having similar features as a second portion of the backtesting data; generating optimal trade size projections, the optimal trade size projections comprising a minimum transaction size where trading activity above this level, at any trade size, demonstrates a statistically insignificant variation in market prices; generating market implied bid-ask spreads, the market implied bid-ask spreads comprising a projected difference between a bid price and an ask price for securities without observable trade data; and generating comparable data groupings, the comparable data groupings comprises a representation of similar bonds determined by one or more statistical and machine learning algorithms.
This invention relates to financial data analysis, specifically methods for determining relationships in backtesting data to improve trading strategies. The problem addressed is the lack of reliable techniques to assess statistical significance, optimal trade sizes, and market spreads in financial datasets, particularly for securities with limited observable trade data. The method involves analyzing backtesting data to identify statistically significant features that influence trading outcomes. Relationship coefficients are generated and applied to portions of the data with similar features, allowing for comparative analysis. Optimal trade size projections are calculated to determine the minimum transaction size where trading activity above this level does not significantly impact market prices. Market-implied bid-ask spreads are generated for securities without observable trade data, providing a projected difference between bid and ask prices. Additionally, comparable data groupings are created using statistical and machine learning algorithms to identify similar bonds or securities, enabling more accurate performance comparisons. These techniques enhance the reliability of backtesting and trading strategy optimization by leveraging data-driven insights.
21. The method of claim 16 , wherein the GUI comprises a results dashboard displaying the each visual/interactive graph of the one or more interactive visuals in a corresponding one or more windows and the backtesting results as backtest details.
This invention relates to a graphical user interface (GUI) for displaying financial or analytical data, specifically for presenting backtesting results in a structured and interactive manner. The GUI includes a results dashboard that organizes multiple interactive visualizations, such as graphs or charts, into separate windows. Each window corresponds to a specific visualization, allowing users to explore different aspects of the backtesting data interactively. The dashboard also displays detailed backtest results, including performance metrics, historical data comparisons, and other relevant analytics. The interactive nature of the visualizations enables users to manipulate the data dynamically, such as adjusting parameters, filtering results, or drilling down into specific details. This approach enhances data analysis by providing a clear, organized, and customizable view of backtesting outcomes, helping users evaluate the effectiveness of financial models or trading strategies. The system improves upon traditional static reports by offering real-time interaction and visualization, making it easier to identify trends, anomalies, or areas for optimization.
22. The method of claim 21 , wherein the one or more windows comprise a magnifier feature that, in response to user input to the GUI, expands the corresponding visual/interactive graph of the one or more interactive visuals to a larger size.
This invention relates to graphical user interfaces (GUIs) for data visualization, specifically improving user interaction with interactive visuals such as graphs and charts. The problem addressed is the difficulty in viewing and manipulating detailed data within small or densely packed visualizations, which can hinder analysis and decision-making. The invention provides a GUI system that includes one or more windows displaying interactive visuals, such as graphs, charts, or other data representations. These visuals are dynamically linked to underlying data sources, allowing real-time updates and user interactions like zooming, panning, or filtering. A key feature is a magnifier tool that, when activated by user input (e.g., clicking or hovering), expands a selected visual to a larger size within the same window or a separate window. This magnified view enhances readability and interaction with fine details without navigating away from the primary interface. The magnifier can be applied to individual visuals or multiple visuals simultaneously, maintaining context and relationships between them. The system may also support additional interactive features, such as tooltips, annotations, or data filtering, to further assist users in analyzing the magnified content. The invention aims to streamline data exploration by reducing clutter and improving focus on relevant details.
23. The method of claim 21 , wherein the one or more interactive visuals comprise a graph illustrating proximity to trade, a graph illustrating a proximity to trade by week, a graph illustrating a distance reduction time series trend analysis, a graph illustrating price percentage distribution analysis, and a graph illustrating an absolute distance reduction days since last trade.
This invention relates to data visualization techniques for analyzing trade proximity and market dynamics. The method provides interactive visual representations to help users assess trade-related metrics over time. The visualizations include a graph showing proximity to trade, which likely measures how close a current trade is to a reference point or historical benchmark. Another graph displays proximity to trade by week, allowing users to observe weekly trends in trade proximity. A distance reduction time series trend analysis graph tracks how the distance between trades changes over time, helping identify patterns or anomalies. A price percentage distribution analysis graph visualizes the distribution of price changes as percentages, providing insights into market volatility or pricing behavior. Finally, a graph illustrating absolute distance reduction days since the last trade measures the time elapsed since the most recent trade, offering context on trade frequency and market activity. These visualizations enable users to monitor trade proximity, price movements, and market trends, aiding in decision-making and strategy development. The interactive nature of the graphs allows users to explore data dynamically, adjusting parameters or focusing on specific timeframes for deeper analysis. This approach is particularly useful in financial markets, supply chain management, or any domain where trade proximity and historical trade data are critical for strategic planning.
24. The method of claim 21 , wherein the backtest details comprise one or more categories of the backtesting results, including CUSIP identification, asset class, sector, number of observations, the one or more categories having a filter for receiving the user input initiating the update the one or more of the displayed backtesting results and the one or more interactive visuals.
This invention relates to financial data analysis, specifically systems for backtesting investment strategies. The problem addressed is the need for detailed, interactive, and customizable visualization of backtesting results to help users refine and validate investment models. The invention provides a method for displaying backtesting results with enhanced filtering and interactive visualizations. Users can input criteria to update displayed results dynamically, allowing for real-time adjustments based on specific parameters. The method includes categorizing backtesting results into one or more groups, such as CUSIP identification, asset class, sector, and the number of observations. Each category has an associated filter that accepts user input to refine the displayed data. When a user adjusts a filter, the system updates the backtesting results and corresponding interactive visuals in response. This allows users to explore different scenarios, compare performance across asset classes or sectors, and identify trends or anomalies in the data. The interactive visualizations may include charts, graphs, or other graphical representations that change dynamically based on the applied filters. The system ensures that users can quickly assess the impact of different variables on investment strategy performance, improving decision-making and strategy optimization.
25. The method of claim 21 , further comprising: in response to a user selecting a portion of a visual/interactive graph via an input to the GUI, generating, by the data conversion module, a new visual/interactive graph for that portion; and displaying, via the data conversion module, the new visual/interactive graph in the one or more windows.
This invention relates to data visualization systems that enhance user interaction with visual/interactive graphs. The problem addressed is the difficulty users face when analyzing large or complex datasets presented in a single graph, as it can be overwhelming and hinder effective data exploration. The solution involves a method for dynamically generating and displaying a new visual/interactive graph when a user selects a portion of an existing graph via a graphical user interface (GUI). The system includes a data conversion module that processes the selected portion to create a new graph, which is then displayed in one or more windows. This allows users to focus on specific subsets of data, improving clarity and usability. The method supports iterative exploration by enabling users to repeatedly select portions of graphs to generate further refined visualizations. The invention is particularly useful in applications requiring detailed data analysis, such as scientific research, financial modeling, or business intelligence, where users need to drill down into specific data segments without losing context. The system ensures seamless interaction by dynamically updating the display based on user input, enhancing the efficiency of data interpretation and decision-making.
26. The method of claim 16 , further comprising: generating, by the data conversion module, a hyperlink for display in the GUI; and in response to the hyperlink being selected via an input to the GUI, generating, by the data conversion module, a pop-up window to appear within a widget displayed in the GUIthe pop-up window comprising contextual information about the data having the plurality of data formats.
This invention relates to data visualization systems that handle multiple data formats within a graphical user interface (GUI). The problem addressed is the difficulty of presenting and interacting with diverse data formats in a unified and user-friendly manner. The system includes a data conversion module that processes data in various formats, such as structured, semi-structured, or unstructured data, and converts it into a standardized format for display in the GUI. The GUI includes widgets that visually represent the data, allowing users to interact with it. The invention further enhances this system by generating a hyperlink within the GUI. When a user selects this hyperlink, the data conversion module generates a pop-up window within a widget. This pop-up window provides contextual information about the data, such as metadata, source details, or additional insights derived from the data. The pop-up window appears seamlessly within the widget, ensuring that the user can access relevant information without navigating away from the current view. This feature improves usability by providing quick access to detailed data context, making it easier for users to understand and analyze the information presented in the GUI. The system is particularly useful in applications requiring the integration and visualization of heterogeneous data sources.
27. The method of claim 16 , wherein the GUI comprises a first page, and wherein the update comprises generating a second page in addition to the first page, the second page displaying the updated backtesting results and the one or more updated interactive visuals.
This invention relates to graphical user interfaces (GUIs) for financial analysis tools, specifically for displaying backtesting results and interactive visualizations. The problem addressed is the need to efficiently present updated backtesting data and corresponding visualizations in a structured and user-friendly manner. The method involves a GUI that initially displays a first page containing backtesting results and interactive visuals. When updates are available, the system generates a second page in addition to the first. The second page presents the updated backtesting results alongside one or more updated interactive visuals, allowing users to compare or analyze the new data without losing access to the original results. The interactive visuals may include charts, graphs, or other data representations that users can manipulate to explore different aspects of the backtesting outcomes. This approach ensures that users can seamlessly transition between the original and updated results, maintaining context while evaluating changes. The system dynamically generates the second page to reflect the latest data, enhancing usability and decision-making in financial analysis workflows. The method is particularly useful in scenarios where backtesting results are frequently updated, such as in algorithmic trading or investment strategy evaluation.
28. The method of claim 16 , wherein the update comprises displaying detailed information about the one or more of the updated backtesting results and the one or more updated interactive visuals in response to the user selecting said one or more of the backtesting results and the one or more interactive visuals via the GUI.
This invention relates to financial data analysis systems, specifically methods for enhancing user interaction with backtesting results and visualizations. The problem addressed is the lack of detailed, on-demand information when users review backtesting outcomes, which can hinder decision-making in financial modeling. The method involves updating a graphical user interface (GUI) to display detailed information about backtesting results and interactive visuals. When a user selects specific backtesting results or visual elements within the GUI, the system dynamically presents additional data related to those selections. This includes metrics, parameters, or contextual insights that were previously summarized or hidden. The interactive visuals may include charts, graphs, or other data representations that users can manipulate to explore different aspects of the backtesting process. The system ensures that users can drill down into specific results or visualizations without navigating away from the main interface, improving efficiency and clarity. This approach is particularly useful in financial analysis, where quick access to detailed performance metrics is critical for evaluating trading strategies or investment models. The method supports real-time or near-real-time updates, allowing users to refine their analysis based on the detailed information provided.
29. The method of claim 28 , wherein the detailed information is displayed in a window within the GUI.
A system and method for displaying detailed information within a graphical user interface (GUI) addresses the challenge of efficiently presenting complex data in a structured and accessible manner. The invention provides a method for organizing and displaying detailed information in a dedicated window within the GUI, ensuring that users can access relevant data without overwhelming the primary interface. The method involves detecting a user interaction, such as a selection or hover action, which triggers the retrieval of detailed information from a data source. This information is then processed and formatted for display in a window that appears within the GUI, allowing users to view the details without navigating away from their current workflow. The window may include interactive elements, such as scroll bars, filters, or expandable sections, to enhance usability. The system ensures that the detailed information is dynamically updated in response to user inputs or changes in the underlying data, maintaining accuracy and relevance. This approach improves user experience by reducing clutter and providing a seamless way to access in-depth information while working within the GUI.
30. The method of claim 28 , wherein the GUI comprises a first page and the detailed information is displayed on a second page.
A graphical user interface (GUI) system is designed to improve data visualization and user interaction in software applications. The system addresses the challenge of efficiently presenting complex information while maintaining a clean and intuitive interface. The GUI includes a first page that provides an overview or summary of data, and a second page that displays detailed information related to the summarized data. The first page may contain simplified representations, such as icons, charts, or brief descriptions, while the second page offers in-depth details, such as expanded data sets, interactive elements, or additional context. The transition between the first and second pages is seamless, allowing users to navigate between high-level summaries and detailed views without disruption. This two-page structure enhances usability by reducing clutter on the primary interface while ensuring that detailed information remains accessible when needed. The system is particularly useful in applications where users need to quickly assess summarized data before diving into specifics, such as in analytics dashboards, project management tools, or data visualization platforms. The design optimizes both efficiency and user experience by balancing brevity and depth in information presentation.
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August 11, 2020
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